scholarly journals A Dynamic Pruning and Feature Selection Strategy for Real-Time Tracking

2005 ◽  
Author(s):  
D.F. Hsu ◽  
D.M. Lyons
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Selection Strategy ◽  
Real Time Tracking

scholarly journals Feature selection for real-time tracking

2006 ◽  
Author(s):  
D. Frank Hsu ◽  
Damian M. Lyons ◽  
Jizhou Ai
Keyword(s):  
Feature Selection ◽  
Real Time ◽  
Real Time Tracking ◽  
Selection For

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

2006 ◽  
Author(s):  
Tian He ◽  
Lin Gu ◽  
Liqian Luo ◽  
Ting Yan ◽  
John A. Stankovic ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Aggregation ◽  
Real Time Tracking

scholarly journals Data integration by two-sensors in a LEAP-based Virtual Glove for human-system interaction

2021 ◽  
Author(s):  
Giuseppe Placidi ◽  
Danilo Avola ◽  
Luigi Cinque ◽  
Matteo Polsinelli ◽  
Eleni Theodoridou ◽  
...  
Keyword(s):  
Data Integration ◽  
Real Time ◽  
Vision System ◽  
Hand Tracking ◽  
Selection Strategy ◽  
Motion Sensors ◽  
Leap Motion ◽  
Time Data ◽  
Lower Velocity ◽  
Human System

AbstractVirtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

Real-time tracking lysosomal pH changes under heatstroke and redox stress with a novel near-infrared emissive probe

Talanta ◽  
2021 ◽  
Vol 228 ◽  
pp. 122184
Author(s):  
Qingfeng Xia ◽  
Shumin Feng ◽  
Jiaxin Hong ◽  
Guoqiang Feng
Keyword(s):  
Real Time ◽  
Near Infrared ◽  
Redox Stress ◽  
Lysosomal Ph ◽  
Ph Changes ◽  
Real Time Tracking ◽  
Emissive Probe

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Real-time tracking of lipid droplets interactions with other organelles by a high signal/noise probe

2021 ◽  
pp. 109366
Author(s):  
Wei Ren ◽  
Dong Wang ◽  
Wei Huang ◽  
Jiajia Li ◽  
Xiaohe Tian ◽  
...  
Keyword(s):  
Real Time ◽  
Lipid Droplets ◽  
High Signal ◽  
Signal Noise ◽  
Real Time Tracking
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